In traditional MapReduce frameworks, a portion of data is divided into large blocks of fixed sizes, replicated, and randomly distributed across the available nodes. With such an approach, however, logically colocated spatial blocks in spatial data sets commonly become physically fragmented on disks. Such blocks subsequently have to be aggregated for most spatial analytics operations, thus incurring significant data movement costs.